蛋白質交互作用是生物體產生各種功能的基礎。透過蛋白質交互作用的研究，可以理解細胞運作的基本原理，進而開發、設計藥物，並針對疾病進行治療。蛋白異構體 (isoform) 是指同一基因經由選擇性剪接 (alternative splicing) 所產生的不同產物，若能進一步了解蛋白異構體的交互作用，不論在基礎或臨床的研究上都非常重要。而近幾年高通量mRNA定序 (RNA-Seq) 提供了蛋白異構體表現量之數據，有利於我們更深入地了解蛋白異構體之交互作用。
本篇研究主要是以貝葉斯機率模型 (Bayesian probability model) 為基礎，有效地整合不同型態的資料，分別是蛋白質異構體表現量 (isoform expression)、蛋白質功能域交互作用 (domain-domain interactions dataset)、基因註解 (gene ontology, GO) 以及直系人類同源蛋白 (orthologous human proteins) 資料，作為我們預測蛋白質異構體交互作用的推論證據。最後，我們將預測結果與其他預測方法進行比較，並且試圖建構出更完整的人類蛋白異構體交互作用網路。Protein interactions are the basis of organism functions. Through protein interaction studies, we can understand the basic principles of cell activity. Then develop and design drugs for the disease treatment. Protein isoforms is generated by alternative splicing from the same gene. If we have deeply understanding of the interactions of protein isoforms, either in basic or clinical research is very important. In recent years, high-throughput mRNA sequencing (RNA-Seq) provides isoform-level expression data, which helps us further understanding the interactions of protein isoforms.
This study is based on Bayesian probabilistic model to effectively integrate different types of information: the mRNA expression, domain-domain interactions, gene annotation and Orthologous human protein datasets, as the inference evidence of isoform-isoform interaction prediction. Finally, we compared the predicted results with other prediction methods, and attempted to construct complete human isoform-isoform interaction networks.